SceneNet \(\rightarrow \)  SUN: Both of the above two settings are for outdoor scenes, and in this setting we consider indoor scenes with SceneNet [1]} as the source and SUN [2]} as the target. The SceneNet dataset has around 5 million simulated images. However, a lot of the images are rather simple with only a few categories in them. Thus, to train the source model, we only choose the top \(50,000\)  images having the highest number of categories of SceneNet. For test-time adaptation, we perform experiments on the test set of SUN containing 5050 images. Both of these datasets contain 13 categories. Specifically, we use the label transformation available with the SceneNet dataset https://github.com/ankurhanda/sunrgbd-meta-data to map the labels in the SUN dataset such that it matches with the label space of SceneNet.
